Search Results (565)

Rotor blades are the primary cause of drone failure. To assess the health status of drone blades, vibration monitoring is required; however, this is challenging due to noisy signals and limited labeled datasets. This study investigates a data loading strategy called selective embedding (SE), which is shown to improve data diagnosis across engineering fields. The hypothesis is that this strategy can improve the classification accuracy of drone blade conditions with multi-axis vibration data. Accelerometer signals are collected under different blade health conditions; the signals are then processed and fed into a deep learning model for multi class condition classification. An ablation study is conducted with different data loading strategies, including traditional single channel, parallel channel, and SE. The results show that SE improves classification accuracy, reduces performance variance, and achieves higher generalization performance across multiple blade fault conditions. These improvements are observed consistently across domain evaluations, where traditional data loading strategies have difficulty generalizing to unseen temporal segments. The findings demonstrate that SE can effectively support vibration fault diagnostics for aerospace applications, offering a reliable way to improve safety in drone monitoring. Full article

In bridge health monitoring, structural responses are strongly coupled with temperature effects and vehicle load effects, making it difficult for conventional fixed thresholds and single data-driven approaches to simultaneously achieve environmental adaptability and quantitative reliability assessment. To address this issue, this study proposes a deep-learning-based dynamic early-warning method for bridge structures, using health-monitoring data from an in-service long-span cable-stayed bridge as the research background. First, a two-month mid-span deflection time series is processed using variational mode decomposition optimized by the Porcupine Optimization Algorithm to separate temperature-induced effects. Subsequently, a hybrid prediction model integrating Informer and SEnet is constructed. Temperature and temperature-induced deflection components are used as input features, and a sliding-window strategy is adopted to achieve high-accuracy prediction of the temperature-induced deflection trend, which serves as the time-varying baseline of the dynamic threshold. On this basis, vehicle load effects are modeled by combining Pareto extreme value theory with finite element analysis and superimposed to establish a two-level dynamic early-warning threshold system that satisfies code requirements. Furthermore, a stochastic finite element Monte Carlo method is introduced to probabilistically model uncertainties associated with material parameters, load effects, and model prediction errors. The threshold failure probability at each time instant is taken as the evaluation metric, enabling quantitative characterization of threshold reliability. The results indicate that under combined multiple working conditions, the proposed method reduces the maximum failure probability of the first-level warning by 32.68% and that of the second-level warning by 93.48%, with more stable and consistent probabilistic responses. In engineering applications, simulation experiments based on stochastic traffic loading show that the warning accuracy is improved by up to 19.27%, while the error rate is reduced by up to 16.16%. The study demonstrates that the proposed method possesses a clear physical and statistical foundation as well as good engineering feasibility and provides a viable pathway for transforming bridge early-warning systems from experience-based schemes toward data-driven and risk-oriented frameworks. Full article

Addressing the issues of the complex mechanical responses and significant spatial effects of asymmetric large-span cable-stayed steel box girder bridges with shared public-rail traffic under operational loads (live load, static wind, and structural temperature differences), this paper uses the Lijiatuo Yangtze River Double-Line Bridge on Chongqing Metro Line 18 as the engineering background to construct multi-scale finite element models for the entire bridge and the closure segment, and validates them against GNSS displacement and strain monitoring data from the actual bridge. The study shows that the spatiotemporal asymmetry of operational live loads induces significant lateral bias effects in the main bridge, resulting in reverse displacements in the mid-span section, and with stress distributions characterized by “oscillation in the side spans and concentration in the mid-span.” The study also shows that, under static wind loads, the bridge’s lateral displacement approximately increases linearly with wind speed, and the mid-span response is higher than that of the side spans, showing significant spatial sensitivity to wind loads. Finally, the study shows that, although the system temperature difference causes small overall displacements, it still induces symmetrical lateral deformations and local stress concentrations near the closure segment. Local refined analyses further reveal the displacement distribution mechanism of the closure segment under operational loads. The health monitoring data agree well with the simulation results, validating the reliability of the numerical model. The research systematically reveals the spatial mechanical behavior of such bridges under operational loads, providing theoretical basis and engineering references for the design optimization and safety monitoring of similar asymmetric cable-stayed bridges. Full article

This study presents an integrated engineering methodology aligned with the planning phase of the ISO 50001:2018 (Energy Management Systems—Requirements with Guidance for Use. International Organization for Standardization (ISO): Geneva, Switzerland, 2018) energy management standard for the design, sizing, and assessment of a solar thermal system applied to domestic hot water production in a medium-scale hotel building. The proposed framework focuses on the energy review stage of ISO 50001, incorporating site-specific climatic assessment, spatial layout optimization, structural feasibility analysis, and energy performance evaluation to support informed technology selection and system viability. Thermal performance is assessed using real operational data from the case study, complemented by a data-driven multivariable regression-based energy performance indicator (EnPI) that relates electricity consumption to cooling degree days and room occupancy. This regression model, developed in accordance with ISO 50001 recommendations, enables transparent monitoring of energy performance under real operating conditions without relying on black-box predictive techniques. Material selection criteria for absorber plates, heat-transfer components, transparent covers, and insulation layers are discussed to support both initial efficiency and performance stability under site-specific climatic conditions. In addition, an indicative and qualitative analysis of material-dependent performance evolution is introduced to support comparative decision-making, without implying quantitative lifetime prediction. Structural feasibility of the collector support system is examined through finite-element simulations under combined gravitational and wind loads, providing illustrative verification of stress distribution under representative operating conditions. The installed system delivers an annual thermal energy contribution of 8468 kWh, resulting in an estimated reduction of 7.79 t of CO${}_2$ emissions per year. Economic indicators suggest a short payback period and a favorable internal rate of return, which should be interpreted as order-of-magnitude estimates within the planning scope of the methodology. Overall, the proposed methodology provides a replicable and multidisciplinary planning-phase framework aligned with ISO 50001 for the design and assessment of solar thermal systems in medium-scale buildings under real operating conditions. Full article

In order to investigate the mechanical properties and supporting effect of the rapidly assembled lattice beam supporting structure in slope engineering, an indoor physical model test based on a scale ratio of 1:2 was carried out to simulate the typical landslide geological conditions of a highway slope. The structural design, construction technology and mechanical response characteristics of the assembled lattice beam under different loads were systematically studied. The stress process of the slope was simulated by the graded vertical loading method, and the evolution law of the soil pressure at each measuring point of the lattice beam cross beam and vertical beam was monitored. The test results show that the assembled lattice beam does not significantly participate in the load transfer of the soil at the initial loading stage. As the load gradually increases, its load-bearing capacity is significantly improved, and the supporting effect is obvious. The earth pressure of the cross beam is non-uniformly distributed along the length direction, and the force near the node and the edge area is significantly higher than that in the mid-span position. The earth pressure of the vertical beam shows a decreasing trend along the height direction, which reveals its transfer law to the concentrated load. The test results can provide a theoretical basis and experimental reference for the design and optimization of a bolt-fabricated lattice beam structure under complex geological conditions. Full article

High-strength bolted connections are fundamental load-bearing components in critical engineering infrastructures such as wind turbines, bridges, and heavy machinery. Under complex service environments involving dynamic loading, vibration, corrosion, and temperature variations, bolts are prone to interacting failure mechanisms, including fatigue fracture, corrosion-assisted cracking, hydrogen embrittlement, and progressive preload loss, which pose significant challenges for reliable condition monitoring and early fault diagnosis. This review provides a structured synthesis of recent advances in bolt health monitoring and intelligent fault diagnosis. A unified framework is established to link multi-physics failure mechanisms with multi-modal sensing technologies and data-driven diagnostic methods. Key sensing approaches—such as piezoelectric impedance techniques, ultrasonic phased array inspection, and computer vision-based monitoring—are critically reviewed in terms of their physical principles, diagnostic capabilities, and limitations. Furthermore, the transition from traditional model-based and signal-processing-driven methods to machine learning- and deep learning-based approaches is examined, with emphasis on multi-modal data fusion, real-time monitoring, and lifecycle-oriented health management enabled by IoT and digital twin technologies. Finally, key challenges and future research directions toward robust and scalable intelligent bolt health management systems are outlined. This review’s primary contribution lies in establishing a novel, integrated framework that links failure physics to sensing and diagnosis, thereby providing a structured roadmap for transitioning from isolated component monitoring to lifecycle-oriented, intelligent health management systems for critical bolted connections. Full article

To analyze the characteristics of acoustic emission (AE) and electromagnetic radiation (EMR) signals in specimens with different water contents during impact loading, impact tests were conducted on sandstone under dry, natural, and saturated conditions using the split Hopkinson pressure bar (SHPB) system. The results show that water reduces the dynamic compressive strength and elastic modulus of sandstone, changes the failure mode from tensile failure to tensile-shear failure, and increases the amount of small-sized fragments after failure. AE and EMR signals effectively reflect the entire deformation process of specimens with different water contents under impact loading. In the elastic stage, only EMR signals appear, indicating that EMR is more sensitive to crack generation. In the yield stage, the AE signal count and energy increase sharply, indicating that the response to specimen failure is better. By comparing AE and EMR signals at different stages, it was found that water inhibits both the propagation and energy of AE and EMR signals. The damage factor D, quantified by AE and EMR counts, accurately represents the damage suffered by specimens with different water contents during impact loading. This study significantly advances the understanding of failure mechanisms in specimens with varying water contents and contributes to practical engineering monitoring of water-bearing rock mass stability. Full article

Semiconductor manufacturing equipment such as vacuum pumps, wafer handling mechanisms, etching machines, and deposition systems operates for a long time under high vacuum, high temperature, strong electromagnetic, and high-precision continuous production environments. Its reliability is directly related to the yield and stability of the production line. During equipment operation, the fault signals are often weak, the noise is strong, and the working conditions are variable, so traditional methods are difficult to achieve high-precision recognition. To solve this problem, this paper proposes a fault diagnosis and health monitoring method for semiconductor manufacturing equipment based on deep learning and subspace transfer. Firstly, considering the cyclostationary characteristics of the operating signals of key equipment, the cyclic spectral analysis technology is used to obtain the cyclic spectral coherence map, which effectively reveals the feature differences under different health states. Then, a deep fault diagnosis model based on the convolutional neural network (CNN) is constructed to extract deep feature representations. Furthermore, the subspace transfer learning technology is introduced, and group normalization and correlation alignment unsupervised adaptation layers are designed to achieve automatic alignment and enhancement of the statistical characteristics of deep features between the source domain and the target domain, which effectively improves the generalization and adaptability of the model. Finally, simulation experiments based on the public bearing dataset verify that the proposed method has strong feature representation ability and high classification accuracy under different working conditions and different loads. Because the key components and experimental scenarios of semiconductor manufacturing equipment have similar signal characteristics, this method can be directly transferred to the early fault diagnosis and health monitoring of semiconductor production line equipment, which has important engineering application value. Full article

Coal mine drill pipes are subjected to periodic impacts and high-intensity loads in complex underground environments, making them prone to developing micro-cracks that gradually expand, leading to equipment failure and major safety accidents. To address this issue, this paper proposes a framework for ultrasonic crack detection in drill pipes, which leverages a sliding-window root mean square (SWRMS) index for feature representation and a convolutional neural network for accurate classification in noisy environments. The influence mechanism of cracks on ultrasonic echoes was studied, and the SWRMS index was introduced to characterize the ultrasonic signal features. This index reflects the spatial position of the crack through the peak position and reveals the crack size through the amplitude, achieving a unified representation of both crack position and size. Furthermore, to address challenges such as spurious echoes and noise interference caused by the drill pipe’s threaded structure in practical engineering applications, convolutional neural network (CNN) was constructed to achieve intelligent identification of drill pipe cracks in high-noise environments. A data augmentation method using alternating noise levels was designed to simulate the scattering effect caused by the drill pipe’s threads and actual noise interference. The results show that CNN exhibits superior recognition performance under different noise levels, maintaining a classification accuracy of 94.4% even at a 75% noise level. The research results verify that the proposed method has significant advantages in crack detection accuracy and noise robustness, providing effective support for real-time monitoring and intelligent diagnosis of key components such as coal mine drill pipes. Full article

To investigate the deformation behavior of corrugated steel pipe arch bridges subjected to differential foundation settlement, this study examines a ten-span continuous corrugated steel pipe arch bridge as the engineering background. A one-year field monitoring program was conducted to record the settlement of each span, and the spatial distribution pattern, annual cumulative settlement, and settlement growth rate were evaluated. Numerical analyses were then performed to compare the deformation response of the bridge under ideal foundation conditions, differential foundation settlement, and vehicle loading. Based on the numerical results, the effectiveness of a concrete lining installed inside the corrugated steel pipe was further assessed. The results show that the settlement of the side spans is significantly larger than that of the middle spans due to the differential foundation settlement in the mining area. The maximum annual cumulative settlement at the side span (span 2) reaches 21.66 mm, which is approximately 4.1 times that of the middle span (span 6). During the monitoring period, the settlement growth rate was high in the early stage (1~3 months), reaching up to 30 percent, and gradually stabilized to about 10 percent per month in the later stage. Compared with the ideal foundation condition, differential settlement increases the pipe stress by a factor of 3.4 and amplifies the deformation by a factor of 9.1. Vehicle loading has a pronounced effect on the deformation of the pipe crown, increasing the settlement by approximately 9 percent, while its influence on the pipe invert is relatively small, with an increase of about 4 percent. Installing a 100 mm thick concrete lining inside the corrugated steel pipe has limited influence on the overall load-carrying behavior but reduces the deformation by 10~20 percent. This reinforcement method is suitable for applications in existing bridges. Full article

Under deep mining conditions, coal and rock masses are subjected to high in situ stress and strong mining-induced disturbances, leading to intensified stress unloading, concentration, and redistribution processes. The stability of surrounding rock is therefore closely related to mine safety. Direct, real-time, and continuous monitoring of in situ stress magnitude, orientation, and evolution is a critical requirement for deep underground engineering. To overcome the limitations of conventional stress monitoring methods under high-stress and strong-disturbance conditions, a novel in situ stress monitoring device was developed, and its performance was systematically verified through laboratory experiments. Typical unloading–reloading and biaxial unequal stress paths of deep surrounding rock were adopted. Tests were conducted on intact specimens and specimens with initial damage levels of 30%, 50%, and 70% to evaluate monitoring performance under different degradation conditions. The results show that the device can stably acquire strain signals throughout the entire loading–unloading process. The inverted monitoring stress exhibits high consistency with the loading system in terms of evolution trends and peak stress positions, with peak stress errors below 5% and correlation coefficients (R²) exceeding 0.95. Although more serious initial damage increases high-frequency fluctuations in the monitoring curves, the overall evolution pattern and unloading response remain stable. Combined acoustic emission results further confirm the reliability of the monitoring outcomes. These findings demonstrate that the proposed device enables accurate and dynamic in situ stress monitoring under deep mining conditions, providing a practical technical approach for surrounding rock stability analysis and disaster prevention. Full article

As a cornerstone of China’s energy infrastructure, the coal mining industry relies heavily on the stability of its underground roadways, where the support of soft rock formations presents a critical and persistent technological challenge. This challenge arises primarily from the high content of expansive clay minerals and well-developed micro-fractures within soft rock, which collectively undermine the effectiveness of conventional support methods. To address the soft rock control problem in China’s Longdong Mining Area, an integrated material–structure control approach is developed and validated in this study. Based on the engineering context of the 3205 material gateway in Xin’an Coal Mine, the research employs a combined methodology of micro-mesoscopic characterization (SEM, XRD), theoretical analysis, and field testing. The results identify the intrinsic instability mechanism, which stems from micron-scale fractures (0.89–20.41 μm) and a high clay mineral content (kaolinite and illite totaling 58.1%) that promote water infiltration, swelling, and strength degradation. In response, a novel synergistic technology was developed, featuring a high-performance grouting material modified with redispersible latex powder and a tiered thick anchoring system. This technology achieves microscale fracture sealing and self-stress cementation while constructing a continuous macroscopic load-bearing structure. Field verification confirms its superior performance: roof subsidence and rib convergence in the test section were reduced to approximately 10 mm and 52 mm, respectively, with grouting effectively sealing fractures to depths of 1.71–3.92 m, as validated by multi-parameter monitoring. By integrating microscale material modification with macroscale structural optimization, this study provides a systematic and replicable solution for enhancing the stability of soft rock roadways under demanding geo-environmental conditions. Soft rock roadways, due to their characteristics of being rich in expansive clay minerals and having well-developed microfractures, make traditional support difficult to ensure roadway stability, so there is an urgent need to develop new active control technologies. This paper takes the 3205 Material Drift in Xin’an Coal Mine as the engineering background and adopts an integrated method combining micro-mesoscopic experiments, theoretical analysis, and field tests. The soft rock instability mechanism is revealed through micro-mesoscopic experiments; a high-performance grouting material added with redispersible latex powder is developed, and a “material–structure” synergistic tiered thick anchoring reinforced load-bearing technology is proposed; the technical effectiveness is verified through roadway surface displacement monitoring, anchor cable axial force monitoring, and borehole televiewer. The study found that micron-scale fractures of 0.89–20.41 μm develop inside the soft rock, and the total content of kaolinite and illite reaches 58.1%, which is the intrinsic root cause of macroscopic instability. In the test area of the new support scheme, the roof subsidence is about 10 mm and the rib convergence is about 52 mm, which are significantly reduced compared with traditional support; grouting effectively seals rock mass fractures in the range of 1.71–3.92 m. This synergistic control technology achieves systematic control from micro-mesoscopic improvement to macroscopic stability by actively modifying the surrounding rock and optimizing the support structure, significantly improving the stability of soft rock roadways. Full article

This study presents the design, implementation, and experimental validation of an integrated fastening monitoring platform for vehicle ground equipment, aimed at supporting structural maintenance and operational safety. Rather than introducing a fundamentally new sensing principle, the work focuses on the system-level integration and verification of existing sensing, communication, and control technologies for reliable bolt loosening detection and torque-controlled pneumatic fastening. The proposed platform consists of a Smart Control Gateway (SCG), a Signal Transducer Socket (STS), and a Smart Washer Set (SWS), incorporating smart nuts and clamping-force sensing washers for M50 and M35 bolts. Sub-GHz wireless RF communication and wired RS-485 transmission are employed to provide scalable and robust connectivity among system components. The SCG hardware and firmware are fully implemented and verified, enabling continuous acquisition and transmission of fastening-state data. Experimental evaluations include functional verification, mechanical integration tests, and durability assessments. The smart washers demonstrate stable sensing performance over 100 assembly and disassembly cycles without observable degradation. The STS is validated through 200,000 impact cycles under intermittent loading conditions (3 s impact, 3 s pause), confirming its suitability for repeated industrial operation. Real-time data transmission tests verify the system’s capability to detect bolt loosening events induced by vibration or external interference. The results indicate that the proposed platform provides a practical and reliable solution for fastening-state monitoring in safety-relevant ground equipment. This work contributes validated engineering evidence for deploying integrated smart fastening systems in industrial maintenance applications and establishes a foundation for future studies on environmental robustness, false-alarm characterization, and real-time performance guarantees. Full article

To support intelligent and sustainable mine engineering, this geotechnics-based study integrates laboratory testing, three-dimensional numerical simulation, and field monitoring to optimize roadway support and improve resource efficiency. This study investigates the geotechnical behavior of the surrounding rock in coalmine roadways under single-face unloading conditions, aiming to provide theoretical and practical support for surrounding rock control in underground coal mining. Excavation of the roadway creates a free surface, leading to unloading, which makes timely support crucial for preventing instability. True-triaxial single-face unloading tests and mechanical tests on hole-containing coal specimens show that the coal exhibits four characteristic stages, namely fissure compaction (closure), elastic deformation, yielding, and residual strength. Under a confining stress of 4 MPa, the peak strength of Coal Seam No. 3 in the true-triaxial single-face unloading test reached 32.4 MPa, whereas the peak strength of the hole-containing coal specimen was only 17.1 MPa, and failure occurred as instantaneous global instability with an “X”-shaped conjugate shear pattern. Numerical simulations were conducted to optimize the roadway’s surrounding rock control scheme, indicating that increasing the bolt length increases the proportion of the load carried by the rock bolts while reducing the load borne by the cable bolts. In addition, advance abutment pressure increases the forces in the support system and amplifies deformation of the solid rib, coal-pillar rib, and roof; roadway surface convergence is dominated by floor heave. Full article

The article presents issues related to fossil fuel energy consumption and CO₂ emissions from motor vehicles. It identifies the main areas of research in this field in the context of motor vehicles, namely driver behavior, fuel consumption, and OBD systems. The research sample consisted of experimental data containing records of a series of test drives conducted with a passenger vehicle equipped with a gasoline-powered internal combustion engine, collected via an OBD diagnostic interface. Three subsets related to engine operation and energy demand patterns were distinguished for the study: during vehicle start-up and low-speed driving (vehicle start-up mode), during urban driving, and during extra-urban driving. Multiple regression models were constructed for the analyzed subsets to predict CO₂ emissions based on engine energy output parameters (power, load) and vehicle kinematic parameters. The developed models were subjected to detailed evaluation and mutual comparison, taking into account their predictive performance and the interpretability of the results. The analysis made it possible to identify the variables with the most substantial impact on CO₂ emissions and fuel energy consumption. The models allow individual drivers to monitor and optimize vehicle energy efficiency in real-time. The extra-urban driving model achieved the highest predictive accuracy, with a mean absolute error (MAE) of 19.62 g/km, which makes it suitable for real-time emission monitoring during highway driving. Full article